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Pilot conversations for sensitive AI systems

A conversation about useful first projects, realistic expectations, and the question of when a pilot is actually able to generate organizational learning.

In this episode we discuss how an initial pilot for a sensitive AI system can be scoped so that it creates more than demo value and actually improves learning quality and decision quality.

Topics covered

  • Which questions should be clarified before a project starts
  • Why oversized pilots usually lose focus
  • Which signals indicate a strong operating context

Three working principles

A pilot becomes useful when it is tightly framed, has visible responsibility, and learns from real cases instead of idealized examples.

That reduces expectation fog and makes progress observable.

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